




'''simple docstring'''




from scipy.stats import pearsonr

import datasets


lowercase__  							=					'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


lowercase__  							=					'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


lowercase__  							=					'\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION     )
class      UpperCAmelCase_     (  datasets.Metric     ):


	"""simple docstring"""


	def  _lowercase    (						self				):
						return datasets.MetricInfo(
						    description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(
						        {
						            "predictions": datasets.Value("float"				),
						            "references": datasets.Value("float"				),
						        }				)						, reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"]						, )







	def  _lowercase    (						self						, UpperCAmelCase_						, UpperCAmelCase_						, UpperCAmelCase_=False				):
						if return_pvalue:
											snake_case_	=      pearsonr(__snake_case						, __snake_case				)
											return {"pearsonr": results[0], "p-value": results[1]}
						else:
											return {"pearsonr": float(pearsonr(__snake_case						, __snake_case				)[0]				)}










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase:   str					=		'Muhammad Umer Farooq'
lowerCAmelCase:   List[str]					=		'MIT'
lowerCAmelCase:   Tuple					=		'1.0.0'
lowerCAmelCase:   List[Any]					=		'Muhammad Umer Farooq'
lowerCAmelCase:   Optional[Any]					=		'contact@muhammadumerfarooq.me'
lowerCAmelCase:   Dict					=		'Alpha'

import re
from html.parser import HTMLParser
from urllib import parse

import requests

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    str      ):
      super().__init__()
      a		:   list[str]	  =	[]
      a		:   List[Any]	  =	domain







    def      lowercase_			( self					:    Dict		,     __snake_case					:    str		,     __snake_case					:    list[tuple[str, str | None]]      ):
      # Only parse the 'anchor' tag.
      if tag == "a":
        # Check the list of defined attributes.
        for name, value in attrs:
          # If href is defined, and not empty nor # print it.
          if name == "href" and value != "#" and value != "":
            # If not already in urls.
            if value not in self.urls:
              a		:   Tuple	  =	parse.urljoin(self.domain		,     __snake_case      )
              self.urls.append(__snake_case      )
def  lowerCamelCase__			(		_A							):
  return ".".join(get_sub_domain_name(_A							).split('.'							)[-2:]							)
def  lowerCamelCase__			(		_A							):
  return parse.urlparse(_A							).netloc
def  lowerCamelCase__			(		_A = "https://github.com"							):
  a		:   Any	  =	get_domain_name(_A							)

  # Initialize the parser
  a		:   Tuple	  =	Parser(_A							)

  try:
    # Open URL
    a		:   List[Any]	  =	requests.get(_A							)

    # pass the raw HTML to the parser to get links
    parser.feed(r.text							)

    # Get links and loop through
    a		:   Union[str, Any]	  =	set()
    for link in parser.urls:
      # open URL.
      # read = requests.get(link)
      try:
        a		:   int	  =	requests.get(_A							)
        # Get the valid email.
        a		:   Optional[Any]	  =	re.findall('[a-zA-Z0-9]+@' + domain					,     read.text							)
        # If not in list then append it.
        for email in emails:
          valid_emails.add(_A							)
      except ValueError:
        pass
  except ValueError:
    raise SystemExit(1							)

  # Finally return a sorted list of email addresses with no duplicates.
  return sorted(_A							)


if __name__ == "__main__":
     lowerCAmelCase:   Any					=		emails_from_url('https://github.com')
     print(F"{len(emails)} emails found:")
     print('\n'.join(sorted(emails)))